In this exercise, you will load a filtered gapminder
dataset - with a subset of data on global development from 1952 - 2007
in increments of 5 years - to capture the period between the Second
World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.
First, start with installing and activating the relevant packages
tidyverse, gganimate, and
gapminder if you do not have them already. Pay
attention to what warning messages you get when installing
gganimate, as your computer might need other packages than
gifski and av
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
#Spørgsmål 1
theme_set(theme_bw())
ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
# scale_x_log10() +
ggtitle("Graf 1952 uden scale_x_log10()")
Let’s plot all the countries in 1952.
theme_set(theme_bw())
ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("Graf 1952 med scale_x_log10()")
…
We see an interesting spread with an outlier to the right. Explore who it is so you can answer question 2 below!
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(data = subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
ggtitle("Graf 2007")
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Answer: why does it make sense to have a log10 scale
(scale_x_log10()) on the x axis? (hint: try to comment
it out and observe the result) Der er en outlier i dokumentet der
betyder at grafen stiger expotentielt
Answer: In Figure 1: Who is the outlier (the richest country in 1952) far right on the x axis?
# Spørgsmål 2
gapminder %>%
filter(year == 1952) %>%
arrange(desc(gdpPercap)) %>%
head(1)
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
labs(title = "Global Development in 1952",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal()
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
labs(title = "Global Development in 2007",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal()
# Spørgsmål 4
gapminder %>%
filter(year == 2007) %>%
arrange(desc(gdpPercap)) %>%
select(country, gdpPercap) %>%
head(5)
## # A tibble: 5 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smooths the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
transition_states() and transition_time()
functions respectively)anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
labs(title = "Global Development in {frame_time}",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal() +
transition_time(year) +
ease_aes('linear')
animate(anim, renderer = gifski_renderer())
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10(labels = scales::comma) +
scale_size_continuous(labels = scales::comma) +
labs(title = "Global Development in {frame_time}",
x = "GDP per capita (log scale)",
y = "Life Expectancy",
color = "Continent") +
theme_minimal() +
transition_time(year) +
ease_aes('linear')+
theme(
plot.title = element_text(size=18, face="bold"),
axis.title.x = element_text(size=16, face="bold"),
axis.title.y = element_text(size=16, face="bold"),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14))
animate(anim, renderer = gifski_renderer())
gapminder_unfiltered dataset or
download more historical data at https://www.gapminder.org/data/ ]birth_year <- 2002
gapminder %>%
filter(year %in% c(birth_year, 2007)) %>%
group_by(year) %>%
summarise(avg_lifeExp = mean(lifeExp, na.rm = TRUE),
avg_gdpPercap = mean(gdpPercap, na.rm = TRUE))
## # A tibble: 2 × 3
## year avg_lifeExp avg_gdpPercap
## <int> <dbl> <dbl>
## 1 2002 65.7 9918.
## 2 2007 67.0 11680.
ggplot(gapminder %>% filter(year %in% c(birth_year, 2007)),
aes(x = factor(year), y = lifeExp, fill = continent)) +
geom_boxplot() +
labs(title = "Life Expectancy Over Time",
x = "Year",
y = "Life Expectancy",
fill = "Continent") +
theme_minimal()
Mellem 2002 og 2007 oplevede verden en generel vækst i både forventet levealder og BNP per capita. Disse tendenser blev drevet af økonomisk ekspansion, medicinske fremskridt og forbedrede levevilkår i mange lande. Forventet levealder steg i de fleste regioner, især i udviklingslande, hvor bedre adgang til sundhedspleje, vaccinationer og forbedret ernæring spillede en central rolle. Afrika oplevede dog en mere moderat stigning på grund af HIV/AIDS-epidemien, der fortsat påvirkede mange lande negativt. I mere udviklede økonomier fortsatte levealderen med at vokse, understøttet af lavere dødelighed fra sygdomme som hjertekarsygdomme og kræft. Samtidig voksede BNP per capita globalt, drevet af økonomisk vækst, stigende handel og teknologiske fremskridt. Kina og Indien oplevede markant vækst i deres BNP per capita takket være industrialisering, eksportboom og øget produktivitet. I Europa og Nordamerika var væksten mere stabil, mens Latinamerika og Østeuropa også nød godt af økonomiske reformer og højere råvarepriser. Sammenhængen mellem BNP per capita og forventet levealder blev tydeligere i denne periode: lande med stigende indkomstniveauer investerede mere i sundhedspleje og infrastruktur, hvilket førte til længere levetid. Selvom ulighed stadig eksisterede, var 2002-2007 præget af generelle forbedringer i menneskers levevilkår verden over.